2025年度技术复盘：从因果推断到系统优化的工程化演进之路

# 因果效应评估引擎核心片段（约800行工程代码节选）
class CausalEstimatorDAG:
    def __init__(self, treatment: str, outcome: str, confounders: List[str]):
        self.treatment = treatment  # 处理变量：是否暴露策略
        self.outcome = outcome      # 结果变量：7日留存
        self.confounders = confounders  # 混杂变量：用户画像、历史行为等
        
    def build_backdoor_adjustment(self, df: DataFrame) -> DataFrame:
        """
        实现后门准则调整：P(Y|do(T)) = Σ_z P(Y|T, Z=z)P(Z=z)
        工程挑战：Z可能包含20+维度，直接分层会导致计算爆炸
        优化：使用倾向性评分降维
        """
        # 1. 构建倾向性评分模型（Propensity Score Model）
        # 使用XGBoost而非LR，捕捉非线性混杂
        ps_model = XGBClassifier(max_depth=6, n_jobs=-1)
        ps_features = self.confounders
        
        # 2. 逆概率加权（IPW）实现
        # 关键：稳定性修剪（trimming）避免极端权重
        df_with_ps = df.withColumn(
            "propensity_score",
            ps_udf(*ps_features)  # 预训练模型的预测UDF
        ).withColumn(
            "ipw_weight",
            when(col("treatment") == 1, 1.0 / col("propensity_score"))
            .otherwise(1.0 / (1.0 - col("propensity_score")))
        ).filter(
            col("propensity_score").between(0.05, 0.95)  # 修剪边界
        )
        
        # 3. 加权回归估计因果效应
        # 使用Sandwich Estimator计算稳健标准误
        final_effect = df_with_ps.groupBy().agg(
            (
                sum(col("outcome") * col("ipw_weight") * col("treatment")) / 
                sum(col("ipw_weight") * col("treatment"))
            ) - (
                sum(col("outcome") * col("ipw_weight") * (1 - col("treatment"))) / 
                sum(col("ipw_weight") * (1 - col("treatment")))
            )
        ).collect()[0][0]
        
        return final_effect

def check_covariate_balance(df: DataFrame, confounders: List[str]) -> Dict[str, float]:
    """
    自动化协变量平衡检查
    返回每个混杂变量在加权后的标准化均值差异
    """
    balance_report = {}
    for var in confounders:
        treated_mean = df.filter(col("treatment") == 1).agg(
            weighted_avg(var, "ipw_weight")
        ).collect()[0][0]
        control_mean = df.filter(col("treatment") == 0).agg(
            weighted_avg(var, "ipw_weight")
        ).collect()[0][0]
        
        pooled_sd = calculate_pooled_sd(df, var)  # 合并标准差
        
        smd = abs(treated_mean - control_mean) / pooled_sd
        balance_report[var] = smd
        
        # 自动触发重匹配逻辑
        if smd > 0.1:
            logger.warning(f"协变量{var}不平衡，SMD={smd:.3f}，建议调整PS模型")
    
    return balance_report

# 稀疏场景因果推断核心代码（PyMC3实现）
def hierarchical_bayesian_inference(
    sparse_data: pd.DataFrame, 
    historical_experiments: List[pd.DataFrame],
    similarity_threshold: float = 0.7
) -> az.InferenceData:
    """
    层次贝叶斯模型：利用历史实验作为先验
    """
    # 1. 计算当前实验与历史实验的协变量相似度
    # 使用Wasserstein距离衡量分布差异
    similar_experiments = []
    for hist in historical_experiments:
        w_dist = calculate_wasserstein_distance(
            sparse_data[self.confounders], 
            hist[self.confounders]
        )
        if w_dist < similarity_threshold:
            similar_experiments.append(hist)
    
    # 2. 构建层次先验
    # 超先验：历史实验效应量的均值与精度
    historical_effects = [exp["effect_size"].mean() for exp in similar_experiments]
    
    with pm.Model() as hierarchical_model:
        # 超参数层
        mu_prior = pm.Normal("mu_prior", mu=np.mean(historical_effects), sigma=1.0)
        tau_prior = pm.Gamma("tau_prior", alpha=1.0, beta=1.0)
        
        # 实验特定效应层
        experiment_effect = pm.Normal(
            "experiment_effect", 
            mu=mu_prior, 
            sigma=1.0 / np.sqrt(tau_prior)
        )
        
        # 观测层
        sigma_likelihood = pm.HalfNormal("sigma", sigma=1.0)
        likelihood = pm.Normal(
            "likelihood",
            mu=experiment_effect * sparse_data["treatment"],
            sigma=sigma_likelihood,
            observed=sparse_data["outcome"]
        )
        
        # 3. NUTS采样与诊断
        trace = pm.sample(
            draws=2000, 
            chains=4, 
            cores=4,
            target_accept=0.95,  # 高接受率保证稀疏数据下的稳定性
            return_inferencedata=True
        )
        
        # 4. 自动诊断：R-hat > 1.1则触发重跑
        if max(az.rhat(trace).values()) > 1.1:
            logger.error("MCMC未收敛，建议增加tune步数或检查模型设定")
        
    return trace

# 工程化封装：与Airflow集成，自动拉取历史实验数据

# JAX加速版本（核心采样逻辑）
def nuts_sampler_jax(log_prob_fn, init_params, num_samples, num_warmup):
    """
    纯JAX实现的NUTS采样器，支持GPU/TPU加速
    """
    # 使用jax.vmap在多个实验间并行
    def single_chain_sampling(rng_key):
        kernel = numpyro.infer.NUTS(log_prob_fn)
        mcmc = numpyro.infer.MCMC(
            kernel, 
            num_warmup=num_warmup, 
            num_samples=num_samples,
            progress_bar=False  # 生产环境关闭进度条
        )
        mcmc.run(rng_key, init_params=init_params)
        return mcmc.get_samples()
    
    # 4个chain并行
    rng_keys = jax.random.split(jax.random.PRNGKey(0), 4)
    samples = jax.vmap(single_chain_sampling)(rng_keys)
    return samples

# 元分析核心代码（使用pymeta包深度定制）
def meta_analytic_pipeline(experiment_ids: List[str]) -> MetaDataset:
    """
    从实验ID列表构建元分析数据集
    """
    # 1. 数据抽取与标准化
    raw_effects = []
    for exp_id in experiment_ids:
        exp_data = fetch_experiment_data(exp_id)
        
        # 根据指标类型选择标准化方法
        if exp_data["metric_type"] == "continuous":
            # 业务收入类：响应比 log(μ_t/μ_c)
            standardized_effect = np.log(
                exp_data["treated_mean"] / exp_data["control_mean"]
            )
            variance = calculate_log_response_ratio_variance(exp_data)
            
        elif exp_data["metric_type"] == "proportion":
            # 留存率类：Cohen's h
            standardized_effect = calculate_cohens_h(
                exp_data["treated_rate"], exp_data["control_rate"]
            )
            variance = calculate_h_variance(exp_data)
        
        raw_effects.append({
            "effect_size": standardized_effect,
            "variance": variance,
            "experiment_id": exp_id,
            "covariates": extract_covariates(exp_data)
        })
    
    # 2. 异质性检验与模型选择
    meta_df = pd.DataFrame(raw_effects)
    heterogeneity_test = het_gals_weights(
        meta_df["effect_size"].values,
        meta_df["variance"].values
    )
    
    i2_statistic = heterogeneity_test.I2
    tau2_estimate = heterogeneity_test.tau2
    
    # 决策逻辑：I² > 50% 强制使用随机效应模型
    if i2_statistic > 50:
        logger.info(f"检测到高异质性(I²={i2_statistic:.1f}%)，采用随机效应模型")
        model = DerSimonianLaird(meta_df)
    else:
        model = FixedEffect(meta_df)
    
    # 3. 亚组分析：按业务线、用户群、实验周期切片
    subgroup_results = {}
    for covariate in ["business_line", "user_segment", "experiment_duration"]:
        grouped = meta_df.groupby(covariate)
        for group_name, group_df in grouped:
            if len(group_df) >= 5:  # 最小实验数阈值
                subgroup_model = RandomEffect(group_df)  # 子组内仍可能异质
                subgroup_results[f"{covariate}_{group_name}"] = {
                    "pooled_effect": subgroup_model.pooled_effect,
                    "confidence_interval": subgroup_model.ci,
                    "i2": subgroup_model.heterogeneity_test.I2
                }
    
    # 4. 风险偏倚评估：Cochrane风险偏倚工具自动化
    bias_assessment = automated_rob_bias_assessment(experiment_ids)
    
    return MetaAnalysisReport(
        overall_model=model,
        subgroup_results=subgroup_results,
        heterogeneity_stats={"I2": i2_statistic, "tau2": tau2_estimate},
        bias_assessment=bias_assessment
    )

-- 分区表创建模板（支持三级分区）
CREATE TABLE IF NOT EXISTS algo_samples (
    sample_id BIGINT,
    user_id INT,
    business_line VARCHAR(20),
    event_time TIMESTAMP,
    features JSONB,
    label FLOAT,
    PRIMARY KEY (sample_id, event_time, business_line, user_mod)
) PARTITION BY RANGE (event_time);

-- 自动创建月分区
CREATE TABLE algo_samples_202501 PARTITION OF algo_samples
    FOR VALUES FROM ('2025-01-01') TO ('2025-02-01')
    PARTITION BY LIST (business_line);

-- 业务线子分区
CREATE TABLE algo_samples_202501_biz_ecom PARTITION OF algo_samples_202501
    FOR VALUES IN ('ecommerce')
    PARTITION BY HASH (user_id);

-- 用户分桶子子分区（128个桶，平衡并行度与文件数）
CREATE TABLE algo_samples_202501_biz_ecom_bucket_0 PARTITION OF algo_samples_202501_biz_ecom
    FOR VALUES WITH (MODULUS 128, REMAINDER 0);
-- ... 重复至bucket_127

-- 性能关键：分区裁剪与索引设计
-- 1. 局部索引（每个分区独立）
CREATE INDEX idx_features_gin ON algo_samples_202501 USING GIN (features);

-- 2. BRIN索引（时序数据高效）
CREATE INDEX idx_event_time_brin ON algo_samples_202501 USING BRIN (event_time) WITH (pages_per_range = 128);

-- 3. 分区级统计信息更新（自动调度）
ANALYZE algo_samples_202501;

# Python调度脚本：分区管理与冷数据归档
def manage_partitions():
    """
    每日调度：创建未来7天分区，归档30天前数据
    """
    conn = psycopg2.connect(DATABASE_URL)
    cur = conn.cursor()
    
    # 1. 自动创建未来分区（使用pg_partman）
    cur.execute("""
        SELECT partman.create_parent(
            'public.algo_samples',
            'event_time',
            'native',
            'monthly'
        );
    """)
    
    # 2. 更新分区策略（保留最近90天）
    cur.execute("""
        SELECT partman.undo_partition(
            'public.algo_samples',
            'archive_table',
            30
        );
    """)
    
    # 3. 冷数据归档到S3（通过aws_s3扩展）
    # 生成Parquet文件并上传
    cur.execute("""
        CREATE EXTENSION IF NOT EXISTS aws_s3 CASCADE;
        
        SELECT * FROM aws_s3.query_export_to_s3(
            'SELECT * FROM algo_samples WHERE event_time < CURRENT_DATE - INTERVAL ''30 days''',
            aws_commons.create_s3_uri('algo-cold-storage', 'samples/', 'us-east-1'),
            options :='format parquet'
        );
    """)
    
    # 4. 替换为外部表，保证查询透明
    cur.execute("""
        CREATE FOREIGN TABLE algo_samples_archive_202410 (
            LIKE algo_samples
        ) SERVER s3_server OPTIONS (
            filename 's3://algo-cold-storage/samples/202410*.parquet'
        );
    """)
    
    conn.commit()
    cur.close()
    conn.close()

-- 原始查询：用户近30天行为聚合（2.3小时）
SELECT 
    user_id,
    event_day,
    COUNT(*) FILTER (WHERE action_type = 'click') as click_cnt,
    AVG(feature->>'dwell_time')::FLOAT as avg_dwell,
    ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY event_day) as day_seq
FROM algo_samples 
WHERE event_time >= CURRENT_DATE - INTERVAL '30 days'
GROUP BY user_id, event_day;

-- EXPLAIN ANALYZE发现问题：Rows Estimate 10万 vs Actual 5亿，导致HashAgg内存溢出

-- 优化步骤1：扩展统计对象（解决JSONB字段选择性估计）
CREATE STATISTICS stats_features_action (ndistinct) ON (features->>'action_type') FROM algo_samples;
CREATE STATISTICS stats_features_dwell (dependencies) ON (features->>'dwell_time', user_id) FROM algo_samples;
ANALYZE algo_samples;

-- 优化步骤2：物化中间结果（窗口函数无法下推）
CREATE MATERIALIZED VIEW user_daily_behavior AS
SELECT 
    user_id,
    event_day,
    COUNT(*) FILTER (WHERE action_type = 'click') as click_cnt,
    AVG(feature->>'dwell_time')::FLOAT as avg_dwell
FROM algo_samples 
WHERE event_time >= CURRENT_DATE - INTERVAL '30 days'
GROUP BY user_id, event_day;
CREATE INDEX ON user_daily_behavior (user_id, event_day);

-- 优化步骤3：使用pg_hint_plan强制Merge Join（避免Nested Loop）
/*+ 
    Leading((udb day_seq_tbl))
    MergeJoin(udb day_seq_tbl)
    Parallel(udb 8)
*/
SELECT 
    udb.user_id,
    udb.event_day,
    udb.click_cnt,
    udb.avg_dwell,
    ds.day_seq
FROM user_daily_behavior udb
JOIN day_sequence_table ds ON udb.event_day = ds.event_day;

-- 优化后：47秒，性能提升176倍

# 查询指纹库与自动优化建议
class QueryFingerprintDB:
    def __init__(self):
        self.conn = psycopg2.connect(DATABASE_URL)
        self.cursor = self.conn.cursor()
        
    def capture_slow_query(self, query: str, execution_time: float):
        """
        捕获慢查询并生成优化建议
        """
        # 1. 生成查询指纹（规范化）
        fingerprint = self.normalize_query(query)
        
        # 2. 记录执行计划
        self.cursor.execute(f"EXPLAIN (FORMAT JSON, ANALYZE) {query}")
        plan = self.cursor.fetchone()[0]
        
        # 3. 模式匹配：识别典型问题
        issues = []
        if self.has_jsonb_field(plan) and not self.has_extended_stats(plan):
            issues.append("缺少JSONB扩展统计，建议CREATE STATISTICS")
        
        if self.has_nested_loop(plan) and self.is_large_table_join(plan):
            issues.append("大表Nested Loop，建议pg_hint_plan强制Hash/Merge Join")
        
        if self.has_filter_misestimate(plan, threshold=100):
            issues.append("选择性估计严重偏差，建议ANALYZE或调整default_statistics_target")
        
        # 4. 自动应用已知优化提示
        if fingerprint in self.known_optimized_hints:
            hint = self.known_optimized_hints[fingerprint]
            optimized_query = f"{hint}\n{query}"
            return optimized_query
        
        return query

-- 基础聚合物化视图（CONCURRENTLY避免锁表）
CREATE MATERIALIZED VIEW CONCURRENTLY MV_user_daily_stats AS
SELECT 
    user_id,
    DATE(event_time) as behavior_day,
    COUNT(*) as total_actions,
    COUNT(DISTINCT session_id) as sessions,
    SUM((features->>'purchase_amount')::DECIMAL) as gmv
FROM algo_samples
WHERE event_time >= CURRENT_DATE - INTERVAL '90 days'
GROUP BY user_id, DATE(event_time);

-- 关键：CONCURRENTLY需要唯一索引
CREATE UNIQUE INDEX ON MV_user_daily_stats (user_id, behavior_day);

-- 增量刷新策略（仅刷新昨日数据）
-- 在Airflow每日调度中执行
REFRESH MATERIALIZED VIEW CONCURRENTLY MV_user_daily_stats 
WHERE behavior_day = CURRENT_DATE - INTERVAL '1 day';

-- 特征宽表：预计算用户画像（JSONB存储）
CREATE MATERIALIZED VIEW MV_user_profile_wide AS
SELECT 
    user_id,
    JSONB_BUILD_OBJECT(
        'click_7d', SUM(click_cnt) FILTER (WHERE behavior_day >= CURRENT_DATE - INTERVAL '7 days'),
        'click_30d', SUM(click_cnt) FILTER (WHERE behavior_day >= CURRENT_DATE - INTERVAL '30 days'),
        'gmv_90d', SUM(gmv) FILTER (WHERE behavior_day >= CURRENT_DATE - INTERVAL '90 days'),
        'sessions_avg', AVG(sessions) FILTER (WHERE behavior_day >= CURRENT_DATE - INTERVAL '30 days')
    ) as profile_features
FROM MV_user_daily_stats
GROUP BY user_id;

-- 交叉特征物化视图：用户-品类偏好（协同过滤用）
CREATE MATERIALIZED VIEW MV_user_category_cross AS
SELECT 
    user_id,
    category_id,
    SUM(interaction_score) as pref_score,
    COUNT(*) as interaction_cnt
FROM (
    SELECT 
        user_id,
        (features->>'category_id')::INT as category_id,
        CASE 
            WHEN action_type = 'purchase' THEN 10
            WHEN action_type = 'click' THEN 1
            ELSE 0.5
        END as interaction_score
    FROM algo_samples
) sub
GROUP BY user_id, category_id;

-- 性能：从即席查询30秒降至物化后5毫秒

# 物化视图依赖管理与自动刷新
class MaterializedViewManager:
    def __init__(self):
        self.view_dependencies = {
            "MV_user_profile_wide": ["MV_user_daily_stats"],
            "MV_user_category_cross": ["algo_samples"],
            "MV_item_daily_stats": ["algo_samples"]
        }
        
    def incremental_refresh(self, view_name: str, date_partition: str):
        """
        增量刷新物化视图，并处理级联依赖
        """
        depends_on = self.view_dependencies.get(view_name, [])
        
        # 1. 自底向上刷新依赖
        for dep in depends_on:
            self.incremental_refresh(dep, date_partition)
        
        # 2. 构建增量刷新SQL
        # 对于基于时间的物化视图，仅刷新指定分区
        refresh_sql = f"""
            REFRESH MATERIALIZED VIEW CONCURRENTLY {view_name}
            WHERE behavior_day = DATE '{date_partition}'
        """
        
        # 3. 执行并监控
        with psycopg2.connect(DATABASE_URL) as conn:
            with conn.cursor() as cur:
                start_time = time.time()
                cur.execute(refresh_sql)
                conn.commit()
                duration = time.time() - start_time
                
                # 4. 性能退化检测：若刷新时间超均值2σ则告警
                if self.is_refresh_anomaly(view_name, duration):
                    send_alert(f"{view_name}刷新异常：{duration:.2f}s")
        
        logger.info(f"{view_name}增量刷新完成，耗时{duration:.2f}s")

# Draft生成与KV Cache复用（PyTorch实现）
class MultiBranchLookaheadEngine:
    def __init__(self, main_model, draft_model, lookahead_k: int = 32):
        self.main_model = main_model  # 70B主模型
        self.draft_model = draft_model  # 7B草稿模型
        self.k = lookahead_k  # 分支数
        
    def generate_drafts(self, input_ids: torch.Tensor, past_key_values: Cache):
        """
        生成k个draft序列，复用past_key_values
        核心优化：不重复计算前缀的KV Cache
        """
        batch_size, seq_len = input_ids.shape
        
        # 1. 采样k个不同的draft策略
        # 策略组合：temperature + top-p + repetition penalty
        draft_configs = self.sample_draft_configs(self.k)
        
        drafts = []
        for config in draft_configs:
            # 关键：复用主模型的past_key_values到草稿模型
            # 通过线性映射层适配维度差异（70B -> 7B）
            adapted_past_kv = self.adapt_kv_cache(
                past_key_values, 
                target_layers=self.draft_model.config.num_hidden_layers
            )
            
            # 2. 并行draft生成（使用vmap）
            draft_ids = self.vmap_generate(
                self.draft_model,
                input_ids,
                adapted_past_kv,
                temperature=config["temp"],
                top_p=config["top_p"],
                max_new_tokens=16  # 短期draft
            )
            drafts.append(draft_ids)
        
        # 3. 返回k*(batch, seq+16)的draft矩阵
        return torch.stack(drafts, dim=1)  # shape: (batch, k, seq+16)
    
    def adapt_kv_cache(self, source_kv, target_layers):
        """
        KV Cache适配：70B模型的80层映射到7B模型的32层
        使用轻量级线性投影层，训练时冻结主模型
        """
        # source_kv: (batch, 80, 2, num_heads, seq, head_dim)
        # target_kv: (batch, 32, 2, num_heads, seq, head_dim)
        
        # 策略：均匀采样 + 线性插值
        step = source_kv.shape[1] // target_layers
        sampled_indices = torch.arange(0, source_kv.shape[1], step)[:target_layers]
        sampled_kv = source_kv[:, sampled_indices]  # 采样
        
        # 投影适配（可训练参数，但训练时只更新投影层）
        projected_kv = self.kv_projection(sampled_kv)
        return projected_kv

# 并行验证的CUDA Kernel融合（Triton编写）
@triton.jit
def parallel_verification_kernel(
    logits_ptr,      # 主模型输出logits
    draft_ids_ptr,   # k个draft序列
    mask_ptr,        # attention mask
    acceptance_ptr,  # 接受结果
    k: tl.constexpr, # 分支数
    vocab_size: tl.constexpr,
    seq_len: tl.constexpr,
):
    """
    Triton Kernel：并行验证k个draft序列
    核心优化：将k次前向融合为一次batch计算
    """
    pid = tl.program_id(axis=0)
    
    # 每个program处理一个token位置的k个draft
    token_idx = pid // k
    draft_idx = pid % k
    
    # 加载主模型在token_idx位置的logits
    logits_offset = token_idx * vocab_size
    logits = tl.load(logits_ptr + logits_offset + tl.arange(0, vocab_size))
    
    # 加载对应draft token id
    draft_id = tl.load(draft_ids_ptr + draft_idx * seq_len + token_idx)
    
    # 计算接受概率：min(1, p_main(draft) / p_draft(draft))
    p_main = tl.exp(logits[draft_id])
    p_draft = tl.load(draft_prob_ptr + draft_idx * seq_len + token_idx)
    
    accept_prob = tl.minimum(p_main / p_draft, 1.0)
    
    # 采样决定
    random = tl.rand(seed, pid)
    is_accept = random < accept_prob
    
    tl.store(acceptance_ptr + draft_idx * seq_len + token_idx, is_accept)

# Python层调用：固化为CUDA Graph
def compile_verification_graph(self, draft_shape):
    """
    将验证逻辑编译为CUDA Graph，避免每次重编译
    """
    # 预热运行
    for _ in range(3):
        logits = torch.randn(*draft_shape, self.vocab_size, device="cuda")
        draft_ids = torch.randint(0, self.vocab_size, draft_shape, device="cuda")
        
        # 捕获计算图
        g = torch.cuda.CUDAGraph()
        s = torch.cuda.Stream()
        s.wait_stream(torch.cuda.current_stream())
        
        with torch.cuda.stream(s):
            for _ in range(3):
                self.verification_pass(logits, draft_ids)  # 实际验证函数
        
        torch.cuda.current_stream().wait_stream(s)
        
        # 开始捕获
        g.capture_begin()
        acceptance = self.verification_pass(logits, draft_ids)
        g.capture_end()
        
        self.verification_graph = g
        self.static_input_logits = logits
        self.static_input_drafts = draft_ids
        self.static_output_acceptance = acceptance
    
    def run_verification(self, logits, draft_ids):
        # 直接 replay Graph，零Python开销
        self.static_input_logits.copy_(logits)
        self.static_input_drafts.copy_(draft_ids)
        self.verification_graph.replay()
        return self.static_output_acceptance

# Tokenization对齐层（运行时开销<2ms）
class TokenizationAligner:
    def __init__(self, main_tokenizer, draft_tokenizer):
        self.main_tokenizer = main_tokenizer
        self.draft_tokenizer = draft_tokenizer
        
        # 预计算映射表（90%的token可直接映射）
        self.draft_to_main = self.build_token_mapping()
        
    def build_token_mapping(self):
        """
        构建草稿token到主token的映射
        策略：优先精确匹配，其次子词分解，最后退回到OOV
        """
        mapping = {}
        for draft_id in range(self.draft_tokenizer.vocab_size):
            draft_token = self.draft_tokenizer.decode([draft_id])
            try:
                main_ids = self.main_tokenizer.encode(draft_token, add_special_tokens=False)
                if len(main_ids) == 1:
                    mapping[draft_id] = main_ids[0]  # 1对1映射
                else:
                    mapping[draft_id] = -1  # 标记为多token，需特殊处理
            except:
                mapping[draft_id] = self.main_tokenizer.unk_token_id
        
        return mapping
    
    def align_draft_sequence(self, draft_ids: torch.Tensor):
        """
        将draft模型的token序列转换为主模型token空间
        """
        # 90%情况：直接查表映射（向量化解码）
        aligned = torch.tensor([
            self.draft_to_main.get(int(did), self.main_tokenizer.unk_token_id)
            for did in draft_ids.flatten()
        ]).reshape(draft_ids.shape)
        
        # 10%情况：子词分解，需动态扩展序列长度
        # 触发回退到自回归生成，牺牲加速比保质量
        if -1 in aligned:
            logger.warning("检测到子词分解token，回退到自回归模式")
            return None  # 信号：无法对齐
        
        return aligned

# 动态lookahead长度控制器
class AdaptiveLookaheadController:
    def __init__(self, init_k=32, min_k=4, max_k=64):
        self.k = init_k
        self.acceptance_history = deque(maxlen=100)  # 滑动窗口
        
    def update_acceptance_rate(self, accepted_tokens: int, total_tokens: int):
        rate = accepted_tokens / total_tokens
        self.acceptance_history.append(rate)
        
        # 滑动平均接受率
        moving_avg = np.mean(self.acceptance_history)
        
        # 控制策略：PI控制器
        # 目标接受率0.6，低于则减k，高于则增k
        error = 0.6 - moving_avg
        
        # 比例-积分控制
        self.k += int(self.kp * error + self.ki * np.sum(self.acceptance_history))
        self.k = np.clip(self.k, self.min_k, self.max_k)
        
        logger.info(f"Adaptive lookahead: k={self.k}, acceptance={moving_avg:.2f}")
    
    def get_lookahead_config(self):
        """返回当前k值和draft策略"""
        if self.k < 10:
            # 小k值：用贪心策略，温度低
            return {"k": self.k, "temp": 0.1, "strategy": "greedy"}
        elif self.k > 50:
            # 大k值：用多样性采样，温度高
            return {"k": self.k, "temp": 0.8, "strategy": "diverse"}
        else:
            return {"k": self.k, "temp": 0.5, "strategy": "balanced"}